Table REF  shows that F-VLM can achieve the state-of-the-art AP\(_r\)  with much fewer epochs. Compared to the current state-of-the-art (ViLD-ENB7), F-VLM can achieve better performance with only 7.4 epochs of training, which is 226\(\times \)  more compute-efficient and 57\(\times \)  faster in wall clock time. We believe the efficiency gain arises from the frozen backbone, which substantially simplifies the learning process. This is orthogonal to the detection-tailored pretraining used by existing works to speed up training [1]}, [2]}. F-VLM system runs almost as fast as a standard detector [3]} at inference time, because the only addition is a single attention pooling layer [4]} on the detected region features (see Fig. REF ).
